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<article id="content">
<header>
<h1 class="title">Module <code>Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import torch,pdb
import torchvision
import torch.nn.modules

class vgg16bn(torch.nn.Module):
    def __init__(self,pretrained = False):
        super(vgg16bn,self).__init__()
        model = list(torchvision.models.vgg16_bn(pretrained=pretrained).features.children())
        model = model[:33]+model[34:43]
        self.model = torch.nn.Sequential(*model)
        
    def forward(self,x):
        return self.model(x)
class resnet(torch.nn.Module):
    def __init__(self,layers,pretrained = False):
        super(resnet,self).__init__()
        if layers == &#39;18&#39;:
            model = torchvision.models.resnet18(pretrained=pretrained)
        elif layers == &#39;34&#39;:
            model = torchvision.models.resnet34(pretrained=pretrained)
        elif layers == &#39;50&#39;:
            model = torchvision.models.resnet50(pretrained=pretrained)
        elif layers == &#39;101&#39;:
            model = torchvision.models.resnet101(pretrained=pretrained)
        elif layers == &#39;152&#39;:
            model = torchvision.models.resnet152(pretrained=pretrained)
        elif layers == &#39;50next&#39;:
            model = torchvision.models.resnext50_32x4d(pretrained=pretrained)
        elif layers == &#39;101next&#39;:
            model = torchvision.models.resnext101_32x8d(pretrained=pretrained)
        elif layers == &#39;50wide&#39;:
            model = torchvision.models.wide_resnet50_2(pretrained=pretrained)
        elif layers == &#39;101wide&#39;:
            model = torchvision.models.wide_resnet101_2(pretrained=pretrained)
        else:
            raise NotImplementedError
        
        self.conv1 = model.conv1
        self.bn1 = model.bn1
        self.relu = model.relu
        self.maxpool = model.maxpool
        self.layer1 = model.layer1
        self.layer2 = model.layer2
        self.layer3 = model.layer3
        self.layer4 = model.layer4

    def forward(self,x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x2 = self.layer2(x)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        return x2,x3,x4</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet"><code class="flex name class">
<span>class <span class="ident">resnet</span></span>
<span>(</span><span>layers, pretrained=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class resnet(torch.nn.Module):
    def __init__(self,layers,pretrained = False):
        super(resnet,self).__init__()
        if layers == &#39;18&#39;:
            model = torchvision.models.resnet18(pretrained=pretrained)
        elif layers == &#39;34&#39;:
            model = torchvision.models.resnet34(pretrained=pretrained)
        elif layers == &#39;50&#39;:
            model = torchvision.models.resnet50(pretrained=pretrained)
        elif layers == &#39;101&#39;:
            model = torchvision.models.resnet101(pretrained=pretrained)
        elif layers == &#39;152&#39;:
            model = torchvision.models.resnet152(pretrained=pretrained)
        elif layers == &#39;50next&#39;:
            model = torchvision.models.resnext50_32x4d(pretrained=pretrained)
        elif layers == &#39;101next&#39;:
            model = torchvision.models.resnext101_32x8d(pretrained=pretrained)
        elif layers == &#39;50wide&#39;:
            model = torchvision.models.wide_resnet50_2(pretrained=pretrained)
        elif layers == &#39;101wide&#39;:
            model = torchvision.models.wide_resnet101_2(pretrained=pretrained)
        else:
            raise NotImplementedError
        
        self.conv1 = model.conv1
        self.bn1 = model.bn1
        self.relu = model.relu
        self.maxpool = model.maxpool
        self.layer1 = model.layer1
        self.layer2 = model.layer2
        self.layer3 = model.layer3
        self.layer4 = model.layer4

    def forward(self,x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x2 = self.layer2(x)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        return x2,x3,x4</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self,x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)
    x = self.layer1(x)
    x2 = self.layer2(x)
    x3 = self.layer3(x2)
    x4 = self.layer4(x3)
    return x2,x3,x4</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn"><code class="flex name class">
<span>class <span class="ident">vgg16bn</span></span>
<span>(</span><span>pretrained=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class vgg16bn(torch.nn.Module):
    def __init__(self,pretrained = False):
        super(vgg16bn,self).__init__()
        model = list(torchvision.models.vgg16_bn(pretrained=pretrained).features.children())
        model = model[:33]+model[34:43]
        self.model = torch.nn.Sequential(*model)
        
    def forward(self,x):
        return self.model(x)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self,x):
    return self.model(x)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane" href="index.html">Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet">resnet</a></code></h4>
<ul class="">
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet.forward" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.resnet.forward">forward</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn">vgg16bn</a></code></h4>
<ul class="">
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn.forward" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLane.backbone.vgg16bn.forward">forward</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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